Efficiently Scaling Transformer Inference (2211.05102v1)

Abstract: We study the problem of efficient generative inference for Transformer models, in one of its most challenging settings: large deep models, with tight latency targets and long sequence lengths. Better understanding of the engineering tradeoffs for inference for large Transformer-based models is important as use cases of these models are growing rapidly throughout application areas. We develop a simple analytical model for inference efficiency to select the best multi-dimensional partitioning techniques optimized for TPU v4 slices based on the application requirements. We combine these with a suite of low-level optimizations to achieve a new Pareto frontier on the latency and model FLOPS utilization (MFU) tradeoffs on 500B+ parameter models that outperforms the FasterTransformer suite of benchmarks. We further show that with appropriate partitioning, the lower memory requirements of multiquery attention (i.e. multiple query heads share single key/value head) enables scaling up to 32x larger context lengths. Finally, we achieve a low-batch-size latency of 29ms per token during generation (using int8 weight quantization) and a 76% MFU during large-batch-size processing of input tokens, while supporting a long 2048-token context length on the PaLM 540B parameter model.

Efficiently Scaling Transformer Inference

The paper presents a comprehensive examination of methodologies to enhance the efficiency of generative inference in Transformer models, specifically focusing on large-scale models, such as those with hundreds of billions of parameters. This is motivated by the growing application of Transformer-based models in various natural language processing tasks, where efficient deployment poses significant challenges due to sequential token generation and considerable computational requirements.

One of the key contributions of the paper is the development of an analytical model to optimize inference efficiency through multi-dimensional partitioning techniques. These partitioning strategies are applied to TPU v4 slices, tailored to address application-specific requirements. The authors employ these strategies alongside a series of hardware-level optimizations to adjust the trade-offs between model FLOPS utilization (MFU) and latency, achieving a new Pareto frontier on these parameters for models exceeding 500 billion parameters. The performance surpasses established benchmarks like those from FasterTransformer.

Significantly, the research finds that by employing multiquery attention—where multiple query heads share a single key/value head—memory requirements are considerably reduced. This innovation permits scaling to context lengths up to 32 times longer than previously manageable. Additionally, the research demonstrates that an exceptionally low latency per token can be realized (29ms) while maintaining substantial MFU levels, particularly using int8 weight quantization for reduced precision operations.

The paper also provides detailed experimental results demonstrating these efficiency improvements. A state-of-the-art 540 billion parameter dense model achieved a notable 76% MFU when processing input tokens in large-batch operations. Further evaluation on PaLM models confirms the scalability of the proposed partitioning strategies, highlighting their general applicability to various model architectures and computational platforms.

In terms of broader implications, this research opens new avenues for deploying large Transformer models more practically within latency-sensitive applications, such as online chatbots, and in environments where high throughput is crucial, such as offline data processing or model distillation tasks. By enhancing the efficiency of such models, the paper contributes to the democratization of access to advanced AI capabilities, making deployment feasible even in settings with constrained computational resources.

Looking to the future, the paper envisions potential advancements through the incorporation of sparsity and adaptive computation techniques to further enhance scaling and efficiency. Such methodologies could offer additional reductions in computational overhead, facilitating even larger models with broader capabilities.

In summary, the paper provides significant insights into engineering solutions necessary for the efficient deployment of large Transformer models, balancing the complexities of latency, throughput, and resource utilization. These advancements promise to impact the practical applicability of large-scale generative models across various domains.